Genetics

Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks.

Environmental sustainability research is dependent on accurate land cover information. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets are derived from a pixel-based, single-date multi-spectral remotely sensed image with an...

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic v...

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single a...

Functional Annotation of Proteins Encoded by the Minimal Bacterial Genome Based on Secondary Structure Element Alignment.

In synthetic biology, one of the key focuses is building a minimal artificial cell which can provide basic chassis for functional study. Recently, the...

A DNA Nanorobot Uprises against Cancer.

DNA-based nanorobots have been shown to sense and respond to molecular triggers, such as intracellular pH and cell surface receptors. A recent report ...

Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.

We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscop...

DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.

A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive a...

Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. ...

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While ti...

Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are u...

Linking of single-molecule experiments with molecular dynamics simulations by machine learning.

Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The for...

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracell...

Prediction of plant lncRNA by ensemble machine learning classifiers.

BACKGROUND: In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to adv...

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-...

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, w...

Semantic segmentation of mFISH images using convolutional networks.

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to detect major chromosomal alterations using fluorescent probes and imaging ...

Optimizing the Use of a Liquid Handling Robot to Conduct a High Throughput Forward Chemical Genetics Screen of Arabidopsis thaliana.

Chemical genetics is increasingly being employed to decode traits in plants that may be recalcitrant to traditional genetics due to gene redundancy or...

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are no...

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be i...

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.

BACKGROUND: Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated ...